{"id":"https://openalex.org/W2970510999","doi":"https://doi.org/10.18653/v1/d19-1507","title":"Subword Language Model for Query Auto-Completion","display_name":"Subword Language Model for Query Auto-Completion","publication_year":2019,"publication_date":"2019-01-01","ids":{"openalex":"https://openalex.org/W2970510999","doi":"https://doi.org/10.18653/v1/d19-1507","mag":"2970510999"},"language":"en","primary_location":{"id":"doi:10.18653/v1/d19-1507","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-1507","pdf_url":"https://www.aclweb.org/anthology/D19-1507.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/D19-1507.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5001186750","display_name":"Gyuwan Kim","orcid":"https://orcid.org/0000-0002-5081-3945"},"institutions":[{"id":"https://openalex.org/I60922564","display_name":"Naver (South Korea)","ror":"https://ror.org/04nzrnx83","country_code":"KR","type":"company","lineage":["https://openalex.org/I60922564"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Gyuwan Kim","raw_affiliation_strings":["NAVER Corp"],"affiliations":[{"raw_affiliation_string":"NAVER Corp","institution_ids":["https://openalex.org/I60922564"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5001186750"],"corresponding_institution_ids":["https://openalex.org/I60922564"],"apc_list":null,"apc_paid":null,"fwci":0.7001,"has_fulltext":true,"cited_by_count":13,"citation_normalized_percentile":{"value":0.78046067,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"5021","last_page":"5031"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9891999959945679,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8130287528038025},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6747283339500427},{"id":"https://openalex.org/keywords/character","display_name":"Character (mathematics)","score":0.6489831209182739},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.5917282700538635},{"id":"https://openalex.org/keywords/prefix","display_name":"Prefix","score":0.5737563371658325},{"id":"https://openalex.org/keywords/rank","display_name":"Rank (graph theory)","score":0.5346279144287109},{"id":"https://openalex.org/keywords/decoding-methods","display_name":"Decoding methods","score":0.48115432262420654},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4686994254589081},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4283742308616638},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.41970232129096985},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4139162003993988},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.2597581744194031},{"id":"https://openalex.org/keywords/linguistics","display_name":"Linguistics","score":0.08563673496246338},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.0802590548992157}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8130287528038025},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6747283339500427},{"id":"https://openalex.org/C2780861071","wikidata":"https://www.wikidata.org/wiki/Q1062934","display_name":"Character (mathematics)","level":2,"score":0.6489831209182739},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.5917282700538635},{"id":"https://openalex.org/C141603448","wikidata":"https://www.wikidata.org/wiki/Q134830","display_name":"Prefix","level":2,"score":0.5737563371658325},{"id":"https://openalex.org/C164226766","wikidata":"https://www.wikidata.org/wiki/Q7293202","display_name":"Rank (graph theory)","level":2,"score":0.5346279144287109},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.48115432262420654},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4686994254589081},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4283742308616638},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.41970232129096985},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4139162003993988},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.2597581744194031},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.08563673496246338},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0802590548992157},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/d19-1507","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-1507","pdf_url":"https://www.aclweb.org/anthology/D19-1507.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/d19-1507","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/d19-1507","pdf_url":"https://www.aclweb.org/anthology/D19-1507.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","display_name":"Reduced inequalities","score":0.699999988079071}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2970510999.pdf","grobid_xml":"https://content.openalex.org/works/W2970510999.grobid-xml"},"referenced_works_count":70,"referenced_works":["https://openalex.org/W1498436455","https://openalex.org/W1522301498","https://openalex.org/W1821462560","https://openalex.org/W1832693441","https://openalex.org/W1938755728","https://openalex.org/W1982858363","https://openalex.org/W1989234058","https://openalex.org/W1993378086","https://openalex.org/W2099649694","https://openalex.org/W2115584760","https://openalex.org/W2119717200","https://openalex.org/W2164986850","https://openalex.org/W2170240176","https://openalex.org/W2300605907","https://openalex.org/W2311921240","https://openalex.org/W2325237720","https://openalex.org/W2337480916","https://openalex.org/W2417736714","https://openalex.org/W2522306626","https://openalex.org/W2525778437","https://openalex.org/W2530486890","https://openalex.org/W2531207078","https://openalex.org/W2549416390","https://openalex.org/W2592647456","https://openalex.org/W2740195281","https://openalex.org/W2745673470","https://openalex.org/W2767206889","https://openalex.org/W2779809129","https://openalex.org/W2787178450","https://openalex.org/W2795247881","https://openalex.org/W2796612419","https://openalex.org/W2798523458","https://openalex.org/W2885588803","https://openalex.org/W2895976713","https://openalex.org/W2896457183","https://openalex.org/W2951559648","https://openalex.org/W2952125979","https://openalex.org/W2952288254","https://openalex.org/W2952487930","https://openalex.org/W2962784628","https://openalex.org/W2962791799","https://openalex.org/W2962964385","https://openalex.org/W2963012544","https://openalex.org/W2963022149","https://openalex.org/W2963077280","https://openalex.org/W2963088785","https://openalex.org/W2963205761","https://openalex.org/W2963250244","https://openalex.org/W2963251942","https://openalex.org/W2963324947","https://openalex.org/W2963341956","https://openalex.org/W2963347649","https://openalex.org/W2963403868","https://openalex.org/W2963434219","https://openalex.org/W2963582782","https://openalex.org/W2963609889","https://openalex.org/W2963736842","https://openalex.org/W2963748792","https://openalex.org/W2963946353","https://openalex.org/W2963979492","https://openalex.org/W2964046515","https://openalex.org/W2964110616","https://openalex.org/W2964121744","https://openalex.org/W2964325845","https://openalex.org/W4289302788","https://openalex.org/W4297797397","https://openalex.org/W4300069726","https://openalex.org/W4300427683","https://openalex.org/W4385245566","https://openalex.org/W4394666973"],"related_works":["https://openalex.org/W3196321793","https://openalex.org/W3080705045","https://openalex.org/W2142481367","https://openalex.org/W2385527937","https://openalex.org/W2005880840","https://openalex.org/W3173108628","https://openalex.org/W4385305499","https://openalex.org/W2507465767","https://openalex.org/W2373597927","https://openalex.org/W2047674875"],"abstract_inverted_index":{"Current":[0],"neural":[1],"query":[2,34],"auto-completion":[3],"(QAC)":[4],"systems":[5],"rely":[6],"on":[7],"character-level":[8,90],"language":[9,25,54],"models,":[10],"but":[11],"they":[12],"slow":[13],"down":[14],"when":[15],"queries":[16,38],"are":[17],"long.":[18],"We":[19],"present":[20],"how":[21,104],"to":[22,75,88,139],"utilize":[23],"subword":[24,53],"models":[26],"for":[27,122,126],"the":[28,89,108,120,142],"fast":[29],"and":[30,61,118],"accurate":[31],"generation":[32],"of":[33,84,144],"completion":[35],"candidates.":[36],"Representing":[37],"with":[39,48,136],"subwords":[40],"shorten":[41],"a":[42,58,62,69,81,95,133],"decoding":[43],"length":[44,101,124],"significantly.":[45],"To":[46],"deal":[47],"issues":[49],"coming":[50],"from":[51],"introducing":[52],"model,":[55],"we":[56,93,131],"develop":[57],"retrace":[59],"algorithm":[60],"reranking":[63],"method":[64],"by":[65],"approximate":[66],"marginalization.":[67],"As":[68],"result,":[70],"our":[71],"model":[72,109],"achieves":[73],"up":[74],"2.5":[76],"times":[77],"faster":[78],"while":[79],"maintaining":[80],"similar":[82],"quality":[83],"generated":[85],"results":[86],"compared":[87],"baseline.":[91],"Also,":[92],"propose":[94],"new":[96],"evaluation":[97],"metric,":[98],"mean":[99],"recoverable":[100],"(MRL),":[102],"measuring":[103],"many":[105],"upcoming":[106],"characters":[107],"could":[110],"complete":[111],"correctly.":[112],"It":[113],"provides":[114],"more":[115],"explicit":[116],"meaning":[117],"eliminates":[119],"need":[121],"prefix":[123],"sampling":[125],"existing":[127],"rank-based":[128],"metrics.":[129],"Moreover,":[130],"performed":[132],"comprehensive":[134],"analysis":[135],"ablation":[137],"study":[138],"figure":[140],"out":[141],"importance":[143],"each":[145],"component":[146],"1":[147],".":[148]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}